Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed ...
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JMIR RESEARCH PROTOCOLS Bunyan et al
Protocol
Expressed Symptoms and Attitudes Toward Using Twitter for
Health Care Engagement Among Patients With Lupus on Social
Media: Protocol for a Mixed Methods Study
Alden Bunyan1, MHDS; Swamy Venuturupalli2, MD; Katja Reuter3,4, PhD
1
Graduate School in Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
2
Division of Rheumatology, Cedars Sinai Medical Center, Los Angeles, CA, United States
3
Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, United States
4
Southern California Clinical and Translational Science Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles,
CA, United States
Corresponding Author:
Katja Reuter, PhD
Department of Public Health and Preventive Medicine
SUNY Upstate Medical University
766 Irving Avenue
Syracuse, NY, 13210
United States
Phone: 1 315 464 1520
Fax: 1 315 464 1701
Email: reuterk@upstate.edu
Abstract
Background: Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million
Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the United States. Social media
provides a platform for patients to find rheumatologists and peers and build awareness of the condition. Researchers have suggested
that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues.
However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter
for engaging them with their health care.
Objective: This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in
the United States between September 1, 2017 and October 31, 2018 to identify patients who publicly discuss their lupus condition
and to assess their expressed health themes and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to
study their attitudes toward using Twitter for engaging them with their health care.
Methods: This is a mixed methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among
lupus patients on Twitter. We used Symplur Signals, a health care social media analytics platform, to access the Twitter data and
analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and
identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via
Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect
data about the characteristics of lupus patients (eg, gender, race/ethnicity, educational level) and their attitudes toward using
Twitter for engaging them with their health care.
Results: This study has been funded by the National Center for Advancing Translational Science through a Clinical and
Translational Science Award. The institutional review board at the University of Southern California (HS-19-00048) approved
the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to “lupus” from
users in the United States published in English between September 1, 2017 and October 31, 2018. We included 40,885 posts in
the analysis. Data analysis was completed in Fall 2020.
Conclusions: The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for
garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a
potential outreach platform for raising awareness of lupus among patients and implementing related health education interventions.
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International Registered Report Identifier (IRRID): DERR1-10.2196/15716
(JMIR Res Protoc 2021;10(5):e15716) doi: 10.2196/15716
KEYWORDS
health promotion; infodemiology; infoveillance; Internet; listening; lupus; systematic lupus erythematosus; surveillance; Twitter;
survey; social media; social network
is public unless a user decides to opt out and make an account
Introduction private [13,14]. Previous research has suggested that Twitter
Background and Rationale provides a “rich and promising avenue for exploring how
patients conceptualize and communicate about their health
Lupus is a chronic disease characterized by an autoimmune issues” [15]. The increasing use of Twitter among members of
response that can range in its frequency and affect any part of disease communities is further evidenced by the abundance of
the body (skin, joints, and organs). It is estimated that at least disease and health topic hashtags used in the messages [16-21].
5 million Americans have lupus, with more than 16,000 new A hashtag is a word or phrase preceded by a hash or pound sign
cases of lupus being reported annually in the United States [1]. (#) and used to identify messages on a specific topic (eg, #lupus,
The condition strikes mostly women of childbearing age, while #spoonies). However, there is little information about the use
women of color are 2-3 times more likely to develop lupus than of social media among lupus patients as well as their attitudes
Caucasian women. However, the disease can present in men toward using Twitter for engaging them with their health care
and children as well. [22].
Lupus is a difficult disease to diagnose as its symptoms can Previous Research on Social Media and Lupus
often mimic those of other diseases [2]. Systemic lupus
erythematosus (SLE), the most common form of lupus, has been The emergence of social media has created new sources of
reported to remain undiagnosed in some populations for an analyzable data [8] and led to new research fields (ie,
average of 6 years [2]. SLE tends to present more abruptly and infodemiology and infoveillance) [7,23]. The data social media
cause more damage in patients of color. This often comes in users generate through their online activities are referred to as
the form of a spike in disease activity called a “flare” and their digital footprint [24] or social mediome [25]. On Twitter,
without treatment, can lead to organ damage and failure. for example, health surveillance researchers have used this data
Therefore, early diagnosis is essential for patients with lupus to gain insight into public perspectives on a variety of diseases
[3]. and health topics such as influenza, autism, schizophrenia,
smoking, and HIV/AIDS [26-31]. In some cases, social media
As most people with lupus develop the disease between the ages user data demonstrated a correlation between disease prevalence
of 15 years and 44 years, we hypothesize that social media and frequency with which Twitter users discussed a disease
provides a potentially promising tool for raising awareness and [32]. The investigators are not aware of lupus-related
supporting early diagnosis and management of lupus. This study surveillance research that involved the social network Twitter.
aims to shed light on the use of Twitter among patients who
publicly discuss their lupus condition on the platform and to However, previous research examined user-generated content
assess their attitudes toward using Twitter to engage them with about lupus on Facebook [33]. The authors looked at the
their health care. representation of health conditions and found that lupus-related
pages ranked the highest for patient support [33]. Additionally,
Social Media a patient commentary highlighted the use of social media, in
The term “social media” describes widely accessible web-based particular Twitter, among lupus patients to find rheumatologists,
and mobile technologies that allow users to view, create, and specialist care, and peers and to build awareness of their health
share information online and to participate in social networking needs and experiences [34]. To our knowledge, there are no
[4-6]. Social media provides both a unique data source for data studies that have leveraged Twitter to improve the understanding
mining of health concerns and related attitudes [7,8] and an of attitudes among patients with lupus.
unprecedented opportunity for delivering information to reach Study Objective and Research Questions
large segments of the population [9] as well as hard-to-reach
subpopulations [10,11]. Today, more than 70% of American This study has two objectives: (1) to conduct a content analysis
adults use at least some type of social media [12]. of Twitter data published by users (in English) in the United
States between September 1, 2017 and October 31, 2018 to
The Social Network Twitter identify patients who publicly discuss their lupus condition and
The social network Twitter is used by 23% of American adults, to assess their expressed health themes and (2) to conduct a
and users are diverse, including Hispanics (25%), Blacks (24%), cross-sectional survey among the lupus patients on Twitter to
and Whites (21%) [12]. Twitter users can post short messages study their attitudes toward using Twitter for engaging them
(tweets) that are limited to 280 characters. They can search for with their health care.
any public message and engage with tweets (ie, they can “like,” Our findings will shed light on whether Twitter provides a
reply, and “retweet” [share] them). By default, Twitter account promising data source for garnering insights and attitudes about
information such as the profile name, description, and location
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lupus expressed among patients. The findings will help to marketing-oriented accounts that could possibly influence the
determine whether Twitter might serve as a potential outreach results and introduce bias [41,42]. We used the program
platform for raising awareness of lupus among patients and BotOrNot [43] to identify those Twitter accounts. Messages
implementing related health education interventions. from these accounts were removed from the dataset to focus on
analyzing patient perspective data. The program BotOrNot
Methods scores a detection accuracy above 95% [43].
This is a mixed methods study that analyzes retrospective Data Collection and Confidentiality
Twitter data and conducts a cross-sectional survey among lupus Database
patients on Twitter.
Study data were collected using the system REDCap (Research
Data Collection Electronic Data Capture) at the University of Southern
This study will analyze user-generated posts in English that California (USC). REDCap is a secure, web-based application
include keywords related to “lupus” (Multimedia Appendix 1) designed to support data capture for research studies [44]. All
from the social network Twitter and were published between analyses will adhere to the terms and conditions, terms of use,
September 1, 2017 and October 31, 2018. To access public and privacy policies of Twitter.
Twitter user data, we used Symplur Signals [35], a health care Twitter Data
social media analytics platform. We limited the dataset to posts
from users with locations in the United States. Any identifying and personal health information was redacted
from the dataset by the coders. Since the “Tweet ID,” “Tweet
Search Filters URL,” “Profile thumbnail URL,” “Username,” and “Display
Twitter posts containing terms related to “lupus” (Multimedia Name” in the dataset can potentially identify the person directly,
Appendix 1) were obtained for the range between September we removed these from the initial data collection sheet and used
1, 2017 and October 31, 2018. We applied the approach a unique code identifier instead. We maintained the link between
suggested by Kim et al [36] to develop the search filters. These the unique code and the identifiable elements in a separate file.
terms can appear in the post or in an accompanying hashtag, We retained the data only for use in this project and destroyed
for example, Lupus or #LupusChat. We selected keywords and the identifiable (Tweet ID, Tweet URL, Profile thumbnail URL,
hashtags based on expert knowledge (clinicians, social media Username, and Display Name) information prior to the data
experts) and used a systematic search of topic-related language analysis as requested by the local IRB.
based on data in Symplur Signals. Survey Data
Data Cleaning and Debiasing The data will be retained in a secure database called REDCap
The following types of irrelevant tweets were excluded: (1) at USC. The anonymous data will be kept for future research.
non-English language tweets identified using the Liu method Individuals are informed that they should not participate in the
[37], (2) retweets (ie, messages shared by Twitter users that study if they do not want their data kept.
other users composed), and (3) messages that originated from Survey Study
outside the United States. Locating users in the United States
was accomplished using a mapped location filter provided by Study Population
Twitter GNIP through the “Profile Geo Enrichment” algorithm The proposed survey study involves contacting lupus patients
(formerly known as GNIP’s Profile Geo 2.0, which was acquired in the United States who discuss their health in English on
by Twitter) [38]. This Twitter data service is among the most Twitter. Eligible survey respondents will be patients with lupus
commonly used data sources in academic Twitter surveillance 18 years of age and older. To focus on feasibility, we will limit
research [39]. To determine a user’s location, the algorithm uses this pilot to lupus patients who discuss their health on Twitter.
a number of data points including the self-reported “Bio Other individuals who talk about how the condition affects a
Location” in the Twitter user profile and geotracking data if family member or friend (eg, parents, siblings) will be excluded
available. The Profile Geo service adds “structured geodata from this study.
relevant to the user location value by geocoding and normalizing
location strings where possible” [38]. Research using a similar Survey Development
multi-indicator method to infer the location of the user showed The goal of the survey is to collect data about the characteristics
the capability of locating 92% of all tweets [40]. However, the of lupus patients (eg, gender, race/ethnicity, educational level)
Profile Geo service attempts to determine the best choice for and their attitudes toward using Twitter for health care
the geographic place described in the profile location string. engagement among lupus patients (eg, How concerned are you
We acknowledge that the results may not be accurate in all cases about researchers using Twitter user information to identify
due to factors such as multiple places with similar names or patients with lupus? How interested are you in getting
ambiguous names. If a value is not provided in a user’s profile information related to lupus via Twitter? How interested are
location field, the Profile Geo service does not provide a you in receiving personalized information about ongoing
classification. research and clinical research opportunities on Twitter?). The
full survey is included in Multimedia Appendix 2.
As we attempt to understand attitudes, we relied on machine
learning to identify Twitter posts by social bots or
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Recruiting Patients With Lupus via Twitter will be unique terms in posts as well as the number of Twitter
We will conduct self-report surveys via Twitter by inviting all messages and users. For each analysis, we will present findings
identified lupus patients who discuss their health on Twitter. in a confusion matrix where the diagonal line indicates the
We will recruit via the project Twitter account using a prevalence of a topic and the off-diagonal lines indicate topic
personalized message package approach (Multimedia Appendix overlap. The number of posts containing 2 or more topics is
3) and replying to a user’s most recent Twitter message where found at the intersection of the matrix for these topics. We will
they mention their lupus condition. Sending multiple messages further describe the patient characteristics such as age, gender,
will allow us to introduce the research project and research team race/ethnicity, and other characteristics and survey responses.
ensuring investigator transparency, ask recipients to follow the We will use multiple regression to assess which variables (eg,
project Twitter account, and remind them of the privacy risks demographics) are significantly associated with acceptance of
of using Twitter. Via the URL link in the message, interested using Twitter for health care engagement. Analyses will be
users will be directed to a webpage (Multimedia Appendix 4) performed in SPSS (v.24), using P=.05 for statistical tests.
that includes more information about the study [45]. The page Sample Size Calculation
will be hosted by the USC Clinical Studies Directory, a public
The sample size estimate (and survey protocol) is based on
tool that allows anyone to search for clinical research studies
previous similar research that demonstrated the usefulness of
at USC. Only those recipients who decide to follow the project
user data to identify and engage cancer patients on Twitter [48].
account will be able to receive the link to the survey via a
private, direct message. The survey will be available in English Twitter data from users in Los Angeles County posted over the
and can be completed on any computer, tablet, or smartphone. course of 12 months were used to identify 134 cancer patients
In the case of no response, reminders will be sent up to 4 weeks who had discussed their cancer condition on Twitter. Nearly
after the initial contact. one-quarter (33/134, 24.63%) of them responded positively to
the outreach on Twitter that was focused on clinical trial
Consent Procedures recruitment. As the prevalence of SLE is lower in the United
Eligible lupus patients who are 18 years and older will proceed States, with 20-150 reported cases per 100,000 [49], compared
to the information sheet form and access the survey once they to the cancer incidence, which is 439 per 100,000 men and
give consent via a check box in the survey form. women per year (based on 2011-2015 cases) [50], we anticipate
a lower number of people who discuss their lupus condition on
Compensation
Twitter. In this pilot study, we anticipate identifying around
Survey participants will be able to enter a raffle to win one of 100-300 Twitter accounts of lupus patients across the United
three US $100 gift cards after they complete the survey. States. We expect that at least 25% of these lupus patients, who
Data Analysis we will contact to participate in the survey study, will complete
the survey.
Coding
Risk Analysis
We will use a standard coding approach for characterizing the
Twitter messages and users. Two independent team members This study presents minimal-risk research. We will use public
will use a range of text classifiers (Multimedia Appendix 5) to data from the social network Twitter. We will de-identify any
identify a priori and emergent code categories in the Twitter subject’s names or Twitter handles, and they will not appear in
posts. We will further characterize the user of the Twitter the analysis dataset. We have implemented a number of
accounts who generated the posts (Multimedia Appendix 6) measures to ensure data security and confidentiality (see Data
based on information available in a user’s Twitter profile (ie, Collection and Confidentiality section). We will further abide
username, description, profile image). Cohen Kappa will be by USC IRB regulations and the USC Privacy of Personal
calculated for each code category to assess interrater reliability Information policy. In general, all data will be entered into a
[46,47]. Average Cohen Kappa greater than 0.8 for all categories computer and database that are password protected. The data
will be considered substantial for this research. The project will be stored using appropriate, secure computer software and
principal investigators will help to build consensus for instances encrypted computers.
where coders disagree. Dissemination of Study Findings
Statistical Analysis The study authors plan to publish the study findings in a
The analyses will rely on public, anonymized data, adhered to peer-reviewed journal and at topic-related conferences (to be
the terms and conditions, terms of use, and privacy policies of determined at a later date). All listed authors or contributors are
Twitter. This study was performed under IRB approval from compliant with guidelines outlined by the International
the authors’ university. No Twitter posts will be reported Committee of Medical Journal Editors for author inclusion in
verbatim in the report of the findings to protect the privacy of a published work. Furthermore, to support research transparency
the users. Representative examples of tweets within each and reproducibility, we will share the de-identified research
category will be selected to illustrate additional themes and will data after publication of the study results. We will share the
be shown as paraphrased quotes. de-identified data on Figshare, a repository where users can
make all of their research outputs available in a citable,
We will use descriptive statistics to analyze the data and identify shareable, and discoverable manner.
the most prevalent topics in the Twitter content. Units of analysis
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non-English languages such as Spanish will not be included. It
Results is also possible that fewer lupus patients discuss their health on
This study was approved by the IRB at USC (Protocol Twitter than we anticipate. We addressed this issue by searching
HS-19-00048; Multimedia Appendix 7). Data extraction and Twitter data from users across the United States. However, even
cleaning are complete. The detailed data extraction and cleaning if we identify lupus patients on Twitter, it is possible that a
flow chart is included in Multimedia Appendix 8. We obtained lower number of them will engage and take the survey. To
47,715 Twitter posts containing terms related to “lupus” from incentivize survey completion, participants who complete the
users in the United States published in English between survey will be able to enter a raffle to win one of 10 US $100
September 1, 2017 and October 31, 2018. After removing gift cards.
duplicates, retweets, non-English tweets, and Twitter posts from Finally, we will take several steps to reduce the chance of
commercial and bot-like accounts, 40,885 posts were included fraudulent survey responses on Twitter, including sharing the
in the analysis. Data analysis was completed in Fall 2020. survey link only via private messages on Twitter once a user
has followed the project account on Twitter. In the case that the
Discussion majority of users seems reluctant to follow the Twitter account,
we will send reminder messages with the personalized link to
Limitations the survey via a public reply message on Twitter to increase the
The generalizability of the study is somewhat limited, and we survey response rate.
recognize that the use of social media data could also lead to
potential bias. Social media research and social media–based Practical Significance
intervention favor those with internet access. Twitter users tend This pilot project will provide preliminary data and practical
to be younger (38% are 18-29 years of age), college graduates insight into the application of publicly available Twitter data
(32%), and located in urban areas (26%) [12]. Nonetheless, it to gain a better understanding of lupus patients who publicly
is worth mentioning that social media users have grown more discuss their condition on Twitter and their attitudes toward
representative of the broader population; for example, they using the platform to engage them with their health care. The
include the Black population (24%) as well as Whites (21%) data will also help to determine whether Twitter might serve as
and Hispanics (25%) [12]. Additionally, Twitter messages from a potential outreach platform for raising awareness of lupus and
locations outside the United States and messages in other, implementing related health interventions.
Acknowledgments
The development of the study protocol and the implementation of the study have been supported by the Southern California
Clinical and Translational Science Institute (SC CTSI) through grant UL1TR000130 from the National Center for Advancing
Translational Sciences (NCATS) of the NIH. The content is solely the responsibility of the authors and does not necessarily
represent the official views of the NIH.
Conflicts of Interest
The authors have no conflicts of interest to report and will not be rewarded in any way, either financially or other by Symplur.com.
Members of the Symplur.com team are neither included in the data analysis nor in the interpretation of the study findings.
Multimedia Appendix 1
Lupus-related keywords and hashtags used for the Twitter search. The selection is based on data from Symplur Signals.
[PDF File (Adobe PDF File), 80 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Survey.
[PDF File (Adobe PDF File), 86 KB-Multimedia Appendix 2]
Multimedia Appendix 3
Twitter recruitment messages.
[PDF File (Adobe PDF File), 48 KB-Multimedia Appendix 3]
Multimedia Appendix 4
Study information page.
[PNG File , 726 KB-Multimedia Appendix 4]
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Multimedia Appendix 5
Coding table used for identifying main themes in lupus-related Twitter posts.
[PDF File (Adobe PDF File), 55 KB-Multimedia Appendix 5]
Multimedia Appendix 6
Code categories to classify Twitter users.
[PDF File (Adobe PDF File), 42 KB-Multimedia Appendix 6]
Multimedia Appendix 7
IRB approval notice.
[PDF File (Adobe PDF File), 1140 KB-Multimedia Appendix 7]
Multimedia Appendix 8
Data extraction and cleaning flow diagram.
[PDF File (Adobe PDF File), 54 KB-Multimedia Appendix 8]
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Abbreviations
IRB: institutional review board
NCATS: National Center for Advancing Translational Sciences
NIH: National Institutes of Health
REDCap: Research Electronic Data Capture
SLE: systemic lupus erythematosus
USC: University of Southern California
Edited by G Eysenbach; submitted 04.08.19; peer-reviewed by O Leal Neto, D Mather; comments to author 31.08.19; revised version
received 28.11.19; accepted 04.02.20; published 06.05.21
Please cite as:
Bunyan A, Venuturupalli S, Reuter K
Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media:
Protocol for a Mixed Methods Study
JMIR Res Protoc 2021;10(5):e15716
URL: https://www.researchprotocols.org/2021/5/e15716
doi: 10.2196/15716
PMID:
©Alden Bunyan, Swamy Venuturupalli, Katja Reuter. Originally published in JMIR Research Protocols
(https://www.researchprotocols.org), 06.05.2021. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The
complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this
copyright and license information must be included.
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